Method for content-based temporal segmentation of video

ABSTRACT

A method for performing content-based temporal segmentation of video sequences, the method comprises the steps of transmitting the video sequence to a processor, identifying within the video sequence a plurality of type-specific individual temporal segments using a plurality of type-specific detectors; analyzing and refining the plurality of type-specific individual temporal segments identified in the identifying the plurality of type-specific individual temporal segments step; and outputting a list of locations within the video sequence of the identified type-specific individual temporal segments.

FIELD OF THE INVENTION

The invention relates generally to the field of visual informationmanagement, and in particular to computer-implemented processing forcontent-based temporal segmentation of video sequences.

BACKGROUND OF THE INVENTION

Efficient representation of visual content of video streams has emergedas the primary functionality in distributed multimedia applications,including video-on-demand, interactive video, content-based search andmanipulation, and automatic analysis of surveillance video. A videostream is a temporally evolving medium where content changes occur dueto camera shot changes, special effects, and object/camera motion withinthe video sequence. Temporal video segmentation constitutes the firststep in content-based video analysis, and refers to breaking the inputvideo sequence into multiple temporal units (segments) based uponcertain uniformity criteria.

Automatic temporal segmentation of video sequences has previouslycentered around the detection of individual camera shots, where eachshot contains the temporal sequence of frames generated during a singleoperation of the camera. Shot detection is performed by computingframe-to-frame similarity metrics to distinguish intershot variations,which are introduced by transitions from one camera shot to the next,from intrashot variations, which are introduced by object and or cameramovement as well as by changes in illumination. Such methods arecollectively known as video shot boundary detection (SBD). Various SBDmethods for temporal video segmentation have been developed. Thesemethods can be broadly divided into three classes, each employingdifferent frame-to-frame similarity metrics: (1) pixelblock comparisonmethods, (2) intensity/color histogram comparison methods, and (3)methods which operate only on compressed, i.e., MPEG encoded videosequences (see K. R. Kao and J. J. Hwang, Techniques and Standards forImage, Video and Audio Coding, Chapters 10-12, Prentice-Hall, N.J.,1996).

The pixel-based comparison methods detect dissimilarities between twovideo frames by comparing the differences in intensity values ofcorresponding pixels in the two frames. The number of the pixels changedare counted and a camera shot boundary is declared if the percentage ofthe total number of pixels changed exceeds a certain threshold value(see H J. Zhang, A, Kankanhalli and S. W. Smoliar, “Automaticpartitioning of full-motion video,” ACM/Springer Multimedia Systems,Vol. 1(1), pp. 10-28, 1993). This type of method can produce numerousfalse shot boundaries due to slight camera movement, e.g., pan or zoom,and or object movement. Additionally, the proper threshold value is afunction of video content and, consequently, requires trial-and-erroradjustment to achieve optimum performance for any given video sequence.

The use of intensity/color histograms for frame content comparison ismore robust to noise and object/camera motion, since the histogram takesinto account only global intensity/color characteristics of each frame.With this method, a shot boundary is detected if the dissimilaritybetween the histograms of two adjacent frames is greater than apre-specified threshold value (see H. J. Zhang, A. Kankanhalli and S. W.Smoliar, “Automatic partitioning of full-motion video”, ACM/SpringerMultimedia Systems, Vol. 1(1), pp. 10-28, 1993). As with the pixel-basedcomparison method, selecting a small threshold value will lead to falsedetections of shot boundaries due to the object and or camera motionswithin the video sequence. Additionally, if the adjacent shots havesimilar global color characteristics but different content, thehistogram dissimilarity will be small and the shot boundary will goundetected.

Temporal segmentation methods have also been developed for use with MPEGencoded video sequences (see F. Arman, A. Hsu and M. Y. Chiu, “ImageProcessing on Compressed Data for Large Video Databases,” Proceedings ofthe 1st ACM International Conference on Multimedia, pp. 267-272, 1993).Temporal segmentation methods which work on this form of video dataanalyze the Discrete Cosine Transform (DCT) coefficients of thecompressed data to find highly dissimilar consecutive frames whichcorrespond to camera breaks. Again, content dependent threshold valuesare required to properly identify the dissimilar frames in the sequencethat are associated with camera shot boundaries. Additionally, numerousapplications require input directly from a video source (tape and orcamera), or from video sequences which are stored in different formats,such as QuickTime, SGImovie, and AVI. For these sequences, methods whichwork only on MPEG compressed video data are not suitable as they wouldrequire encoding the video data into an MPEG format prior to SBD.Additionally, the quality of MPEG encoded data can vary greatly, thuscausing the temporal segmentation from such encoded video data to be afunction of the encoding as well as the content.

The fundamental drawback of the hereinabove described methods is thatthey do not allow for fully automatic processing based upon the contentof an arbitrary input video, i.e., they are not truly domainindependent. While the assumption of domain independence is valid forcomputation of the frame similarity metrics, it clearly does not applyto the decision criteria, particularly the selection of the thresholdvalues. Reported studies (see D. C. Coil and G. K. Choma, “ImageActivity Characteristics in Broadcast Television,” IEEE Transactions onCommunication, pp. 1201-1206, Oct. 1976) on the statistical behavior ofvideo frame differences clearly show that a threshold value that isappropriate for one type of video content will not yield acceptableresults for another type of video content.

Another drawback of the hereinabove methods is that they arefundamentally designed for the identification of individual camerashots. i.e., temporal content changes between adjacent frames. Completecontent-based temporal segmentation of video sequences must also includeidentification of temporal segments associated with significant contentchanges within shots as well as a the temporal segments associated withvideo editing effects, i.e., fade, dissolve, and uniform intensitysegments. Methods have be developed to specifically detect fade (U.S.Pat. No. 5,245,436) and dissolve (U.S. Pat. No. 5,283,645) segments invideo sequences, but when any of the hereinabove methods are modified inan attempt to detect the total set of possible temporal segments, theirperformance is compromised. Such modifications commonly require morecontent dependent thresholds, each of which must be established for thespecific video content before optimum performance can be achieved.

Therefore, there is a need for a method and system for performingaccurate and automatic content-based temporal segmentation of videosequences.

SUMMARY OF THE INVENTION

The present invention is directed to overcoming the problems set forthabove. One aspect of the invention is directed to a method forperforming content-based temporal segmentation of video sequencescomprising the steps of: (a) transmitting the video sequence to aprocessor; (b) identifying within the video sequence a plurality oftype-specific individual temporal segments using a plurality oftype-specific detectors; (c) analyzing and refining the plurality oftype-specific individual temporal segments identified in step (b); and(d) outputting a list of locations within the video sequence of theidentified type-specific individual temporal segments.

It is accordingly an object of this invention to overcome the abovedescribed shortcomings and drawbacks of the known art

It is still another object to provide a computer-implemented method andsystem for performing accurate automatic content-based temporalsegmentation of video sequences.

Further objects and advantages of this invention will become apparentfrom the detailed description of a preferred embodiment which follows.

These and other aspects, objects, features, and advantages of thepresent invention will become more fully understood and appreciated froma review of the following description of the preferred embodiments andappended claims, and by reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is block schematic of a computer-implemented method forcontent-based temporal segmentation of video sequences;

FIG. 2 is a detailed flow chart of the shot boundary detection componentof the method;

FIG. 3 illustrates the individual frame color component histograms andcolor histogram difference for two adjacent frames of a video sequence;

FIG. 4 is a temporal plot of the frame color histogram differences thatillustrates the process of elimination of false positives;

FIG. 5 is detailed flow chart of the uniform segment detection componentof the method;

FIG. 6 is a detailed flow chart of the fade segment detection componentof the method;

FIG. 7 is a temporal plot of the difference in frame color histogramvariance that illustrates the process of detecting fade segments whichare associated with uniform segments;

FIG. 8 is a diagram illustrated the format of the list of temporalsegment locations; and

FIG. 9 is a flow chart of an alternative embodiment of the inventionthat performs temporal segmentation of a video sequence using temporalwindows.

To facilitate understanding, identical reference numerals have beenused, where possible, to designate identical elements that are common tothe figures.

DETAILED DESCRIPTION OF THE INVENTION

As used herein, computer readable storage medium may comprise, forexample, magnetic storage media such as magnetic disk (such as floppydisk) or magnetic tape; optical storage media such as optical disc,optical tape, or machine readable bar code; solid state electronicstorage devices such as random access memory (RAM), or read only memory(ROM); or any other physical device or medium employed to store acomputer program or data. A processor as used herein can include one ormore central processing units (CPUs).

A video sequence as used herein is defined as a temporally orderedsequence of individual digital images which may be generated directlyfrom a digital source, such as a digital electronic camera or graphicarts application on a computer, or may be produced by the digitalconversion (digitization) of the visual portion of analog signals, suchas those produced by television broadcast or recorded medium, or may beproduced by the digital conversion (digitization) of motion picturefilm. A frame as used herein is defined as the smallest temporal unit ofa video sequence to be represented as a single image. A shot as usedherein is defined as the temporal sequence of frames generated during asingle operation of a capture device, e.g., a camera. A fade as usedherein is defined as a temporal transition segment within a videosequence wherein the pixels of the video frames are subjected to achromatic scaling operation. A fade-in is the temporal segment in whichthe video frame pixel values change from a spatially uniform value(nominally zero) to their normal values within the shot. Conversely, afade-out is the temporal segment in which the video frame pixel valueschange from their normal values to a spatially uniform value (nominallyzero). A dissolve as used herein is defined as a temporal transitionsegment between two adjacent camera shots wherein the frame pixels inthe first shot fade-out from their normal values to a zero pixel valueconcurrent with a fade-in of the frame pixels in the second shot from azero pixel value to their normal frame pixel values.

As used herein, a temporal segment comprises a set of temporallyconsecutive frames within a video sequence that contain similar content,either a portion of a camera shot, a complete camera shot, a cameragradual transition segment (fade or dissolve), a blank content (uniformintensity) segment, or an appropriate combination of one or more ofthese. Temporal segmentation refers to detection of these individualtemporal segments within a video sequence, or more correctly, detectingthe temporal points within the video sequence where the video contenttransitions from one temporal segment to another. In order to detect theboundary between temporally adjacent segments, successive frame pairs inthe input video sequence are processed by a computer algorithm to yieldframe content comparison metrics that can be subsequently used toquantify the content similarity between subsequent frames.

Referring to FIG. 1, there is shown a schematic diagram of acontent-based temporal segmentation method. The input video sequence 110is processed 120 to determine the locations of the temporal segments 130of the video sequence 110. Accurate detection of the different types oftemporal segments within a video sequence requires that separate methodsbe employed, one for each type of temporal segment. Therefore, theprocess 120 of determining the locations of temporal segments 130 isachieved by the application of four type-specific temporal segmentdetection methods. Specifically, the method of content-based temporalsegmentation 120 comprises detecting 140 camera shot boundaries (i.e.,cuts), detecting 150 fade-in and fade-out segments, detecting 160dissolve segments, and detecting 170 uniform color/gray level segments.The output from these individual detection processes is a list 145 ofshot boundary locations, a list 155 of fade segment locations, a list165 of dissolve segment locations, and a list 175 of uniform segmentlocations. These four lists of temporal segment locations are analyzedand refined 180 in order to resolve conflicts that may arise among thefour detection processes and to consolidate the four lists into a singlelist 130 of temporal segment locations. Each of the type-specifictemporal segment detection methods will be discussed in detailhereinbelow.

Shot Boundary Detection

Referring now to FIG. 2, the method of camera shot boundary (cut)detection 140 involves the computation of multiple frame comparisonmetrics in order to accurately detect the locations in the videosequence in which there is significant content change betweenconsecutive frames, i.e., camera shot boundaries. In the preferredembodiment of the present invention, two different frame comparisonmetrics are computed. The first is a frame-to-frame color histogramdifference metric 210 which is a measure of the color similarity ofadjacent frames in the video sequence 110 This metric, as statedhereinbefore, is sensitive only to global color changes and relativelyinsensitive to object/camera motion. At camera shot boundaries, due tothe sudden change in frame content characteristics, this metric willtake on a value higher than that within a camera shot. However,different shots can have very similar color characteristics while havingsignificantly different content, thus producing a small value in thecolor histogram frame difference metric at the shot boundary. Therefore,the color histogram frame difference metric 210 is supplemented with apixel intensity frame difference metric 220, which is more sensitive tospatially localized content changes. This frame pixel difference metric220 is a measure of the spatial similarity of adjacent frames in thevideo sequence 110 and will produce a large value at shot boundarieseven when the color characteristics of the two shots are similar.However, this metric is more sensitive to local spatial contentvariations within a shot. Therefore, the output from these two metricsis combined to produce a more reliable indication of the true shotboundary locations.

The color histogram frame difference metric 210 is computed as thepairwise color histogram absolute difference between two successiveframe histograms:${HD} = \frac{\sum\limits_{j}{{{H_{I - 1}(j)} - {H_{I}(j)}}}}{NP}$

where HD is the color histogram absolute difference comparison metric,

H_(I-1)(j) is the jth element of the histogram from frame I-1,

H_(I)(j) is the jth element of the histogram from frame I, and

NP is the number of pixels in the frame image.

The color histogram H_(I)(j) of each frame is computed from 24 bit YCbCrcolor pixel values. Color histograms for each component are computedindividually and then concatenated to form a single histogram (see FIG.3). Those skilled in the art will recognize that other color spaces,such as RGB, YIQ, L*a*b*, Lst, or HSV can be employed without departingfrom the scope of the invention. Additionally, multidimensionalhistograms or other methods for color histogram representation, as wellas an intensity or luminance only histogram may be employed forhistogram computation without departing from the scope of the invention.The selected color space can also be quantized to yield a fewer numberof bins for each color component histogram.

The pixel intensity frame difference metric 220 is computed as$\begin{matrix}{{{PD}( {x,y} )} = 1} & {{{if}\quad {{{F_{I - 1}( {x,y} )} - {F_{I}( {x,y} )}}}} > {NV}} \\0 & {else}\end{matrix}$

Then${FPD} = \frac{\sum\limits_{x}{\sum\limits_{y}{{PD}( {x,y} )}}}{NP}$

where PD(x,y) is the pairwise pixel difference at location (x,y)

F_(I-1)(x,y) is the pixel value at location (x,y) in frame I-1,

F_(I)(x,y) is the pixel value at location (x,y) in frame I,

NV is a noise value which PD(x,y) must exceed,

FPD is the frame pixel difference metric, and

NP is the number of pixels in the frame image.

The frame pixel value used in F_(I)(x,y) and F_(I-1)(x,y) is computed asa weighted sum of the pixel color component values at location (x,y) inframes I and I-1 respectively. The noise value NV, used to reduce themetric's sensitivity to noise and small inconsequential content changes,is determined empirically. In the preferred embodiment, a value of 16for NV has been determined to be adequate to provide the desired noiseinsensitivity for a wide variety of video content. Those skilled in theart will recognize that the pixel intensity frame difference can becomputed from pixel values in various color spaces, such as YCbCr, RGB,YIQ, L*a*b*, Lst, or HSV without departing from the scope of theinvention. Additionally, the selected pixel value space can be quantizedto yield a reduced dynamic range, i.e., fewer number of pixel values foreach color component histogram.

The color histogram frame difference HD) 210 and the pixel intensityframe difference FPD 220 are computed for every frame pair in the videosequence 110. Notice that no user adjustable threshold value is employedin the computation of either metric. Both sets of differences are passedinto a k-means unsupervised clustering algorithm 230 in order toseparate the difference data into two classes. This two class clusteringstep 230 is completely unsupervised, and does not require anyuser-defined or application-specific thresholds or parameters in orderto achieve optimum class separation. The k-means clustering 230 is awell known technique for clustering data into statistically significantclasses or groups (see R. O. Duda and P. E. Hart, Pattern Classificationand Scene Analysis, pp. 201-202, Wiley, New York, 1973), the details ofwhich will not be discussed herein. Those skilled in the art willappreciate that other cluster algorithms (see A. K. Jain and R. C.Dubes, Algorithms for Clustering Data, Prentice-Hall, New Jersey, 1988)can be employed to separate the data into two classes without departingfrom the scope of the invention. The k-means algorithm performs twoclass clustering on the frame comparison metrics iteratively, until theclustering process converges to two distinct classes 240, onerepresenting the potential shot boundary locations and the otherrepresenting the non-shot boundary locations. The set of non-shotboundary locations is normally deleted

The set of potential shot boundary locations contains both true shotboundary locations and a number of non-shot boundary locations (falsepositives) due to the overlap of the two classes in feature space afterclustering. Therefore, the set of potential shot boundary locations isanalyzed and refined 250 using the data from the set of color histogramframe differences. Referring now to FIG. 4, this refinement isaccomplished by examining the color histogram frame differences for alocal maxima at each location identified 410 as a potential shotboundary in the set of potential shot boundary locations. Two casesexist for refinement of the potential shot boundary locations:

Case (i)- If no other potential shot boundary exists within ±D1 framesof this location, then the frame histogram difference metric value mustbe greater than the metric value on either side by X1% to be a shotboundary. If so, then leave the location in the set of potential shotboundary locations. If not, then discard this location from the set ofpotential shot boundary locations.

Case (ii)- If another potential shot boundary exists within ±D1 framesof this location, then the frame histogram difference metric value mustbe greater than the metric value on either side by X2 % to be a shotboundary, where X2 is greater than X1. If so, then leave the location inthe set of potential shot boundary locations. If not, then discard thislocation from the set of potential shot boundary locations.

The optimum values for the parameters D1, X1, and X2 can be determinedempirically. In the preferred embodiment, the values for D1, X1, and X2are preset to 11, 06%, and 12% respectively. These values have beenshown to yield excellent performance on video sequences containing awide variety of content.

The result of this refinement 250 is the elimination of false positivelocations from the list of potential shot boundaries, resulting in thefinal list 145 of shot boundary locations within the video sequence,each identified by numerical frame number. Those skilled in the art willappreciate that other frame comparison metrics can be used in eitherplace of or in conjunction with the color histogram and pixel differencemetrics described hereinabove without departing from the scope of theinvention. Functions such as difference in frame differences, absoluteframe differences, chi-square test for color histogram comparison, orany other function that yields sharp discontinuities in the computedmetric values across shot boundaries while maintaining a low level ofactivity within individual shots can be employed. Furthermore, thecomparison function may be computed over the entire frame, or onlywithin a certain predefined spatial window within the frame, or overcorresponding multiple spatial segments within successive frames.Multiple functions for frame comparison can be computed for every framepair and all features may simultaneously be utilized as elements of afeature vector representing frame similarities. These feature vectorsmay then be employed in the clustering algorithm described hereinabove,and the shot boundary detection threshold may be obtained in theN-dimensional feature space. Alternatively, in place of computing theframe comparison metrics from the actual video sequence frames, suchcomparison metrics can be derived from difference images, motionvectors, DC images, edge images, frame statistics, or the like, whichthemselves are derived from the individual frames of the video sequence.Prior to clustering, the calculated frame comparison metrics can bepreprocessed using median filtering, mean filtering, or the like, toeliminate false discontinuities/peaks that are observed due to contentactivity within a shot segment Additionally, the input video sequencecan be temporally sampled, and individual frames in the video sequencemay be spatially sampled to reduce the amount of data processing inorder to improve algorithm speed and performance.

Uniform Segment Detection

Retuning now to FIG. 1, the video sequence 110 is also analyzed todetect 170 uniform temporal segments. Such segments frequently occur invideo sequences in order to add a temporal spacing, or pause, in thepresentation of content. The computed frame color histogram data used inthe shot boundary detection as described hereinabove is also utilizedfor detecting temporal segments of uniform color/intensity. Referring toFIG. 5, the mean and variance of the individual color components in thecolor histogram are computed 510 for each frame in the video sequence110: ${HM}_{I} = {\frac{1}{NP}{\sum\limits_{j}{{jH}_{I}(j)}}}$

where HM_(I) is the histogram mean value for frame I,

H_(I)(j) is the j^(th) histogram value for frame I, and

NP is the number of pixels in frame I, and${HV}_{I} = {\frac{1}{NP}{\sum\limits_{j}{j( {j - {HM}_{I}} )}^{2}}}$

where HV_(I) is the histogram variance value for frame I.

If a frame has a luminance component variance less than a predeterminedamount V1, then that frame is selected 520 as a uniform frame and itstemporal location is appended to the list 175 of uniform segmentlocations. All frames in the sequence are processed 525 to initiallylocate the potential uniform frames. This process is followed by arefinement process 530 to group the identified frames into contiguoustemporal segments. In that process 530, if a uniform frame has beenpreviously identified D2 frames prior, then all intermediate frames areselected as uniform and their temporal locations are appended to thelist 175 of uniform segment locations. Finally, if the number oftemporally adjacent frames in the uniform segment is less than M1 (theminimum number of frames that can constitute a uniform temporalsegment), then delete the temporal locations of these frames from thelist 175 of uniform segment locations. The optimum values for theparameters D2, V1, and M1 can be determined empirically. In thepreferred embodiment, the values of D2, V1, and M1 are preset to 3, 0.1,and 15 respectively. These values have been shown to yield excellentperformance on video sequences containing a wide variety of content. Thefinal result of this uniform segment detection process 170 is a list 175of uniform segment locations within the video sequence 110, eachidentified by a start frame and end frame number.

Fade Segment Detection

Referring to FIG. 1, the video sequence 110 is now analyzed 150 todetect fade-in-fade-out temporal segments. Fade segments in the videosequence 110 are temporally associated with uniform temporal segments,i.e., a fade segment will be immediately preceded or proceeded by auniform segment The beginning of each uniform temporal segment maycorrespond to the end of a fade-out segment. Likewise, the end of eachuniform temporal segment may correspond to the beginning of a fade-insegment. Thus, it is sufficient to carry out fade temporal segmentdetection on the endpoints of every isolated uniform temporal segment.

Referring to FIGS. 6 and 7, fade detection begins by locating 605 eachof the uniform segments in the video sequence 110 previously identifiedby the uniform segment detection 170. The endpoints of each uniformsegment 705, i.e., the beginning 710 and end 720 frames, are temporallysearched over a immediately adjacent temporal window 720 of length W.For fade-out detection 610, frame index I is set to the first frame 710of the uniform temporal segment 705. The difference in the colorhistogram variance between frames I-1 and I is computed as

A _(FO) =HV _(I) −HV _(I—1)

If this difference A_(FO) is greater than zero but less than an amountΔHV, then frame I-1 is labeled as a fade-out frame. The frame index I isdecremented, and the differences in color histogram variance areobserved in a similar manner for all the frames that lie inside thewindow 730 of size W. If at any point in the analysis the colorhistogram variance difference A_(FO) exceeds an amount ΔHV_(max), thenthe fade-out detection process 610 is terminated and fade-in detection620 is initiated within the window 730 at the opposite end of theuniform temporal segment 705.

The interframe variance difference A_(FO) may sometimes fall below zero,due to noise in the subject frames or minute fluctuations in theluminance characteristics. In order to avoid mis-classifications due tosuch effects, the difference between I-2 and I is considered if thevariance difference between frames I-1 and I falls below zero. If thissecond difference is found to be above zero, and if the variancedifference B between frames I-2 and I-1 is found to satisfy theconditions 0<B<ΔHV, then frame I-1 is labeled as a fade-out frame andfade-out detection 610 proceeds as before.

For fade-in identification 620, frame index I is set to the last frame720 of the uniform temporal segment 705. The difference in the colorhistogram variance between frames I+1 and I is computed as

A _(FI) =HV _(I+1) −HV _(I)

If this difference A_(FI) is greater than zero but less than an amountΔHV, then frame I+1 is labeled as a fade-in frame. The frame index I isincremented, and the differences in color histogram variance areobserved in a similar manner for all the frames that lie inside thewindow 730 of size W. If at any point in the analysis the colorhistogram variance difference A_(FI) exceeds an amount ΔHV_(max), thenthe fade-out detection process 620 is terminated, and the nextpreviously identified uniform temporal segment in the video sequence issimilarly analyzed. As with the detection 610 of fade-out temporalsegments, the interframe variance difference A_(FI) may sometimes fallbelow zero, due to noise in the subject frames or minute fluctuations inthe luminance characteristics. In order to avoid mis-classifications dueto such effects, the difference between I+2 and I is considered if thevariance difference between frames I+1 and I falls below zero. If thissecond difference is found to be above zero, and if the variancedifference B between frames I+2 and I+1 is found to satisfy theconditions 0<B<ΔHV, then frame I+1 is labeled as a fade-in frame andfade-in detection 610 proceeds as before. This process continues untilall detected uniform temporal segments have been similarly analyzed.

When all frames within the window 730 have been processed for eitherfade-in or fade-out, fade detection is terminated, regardless of whetherthe variance differences continue to satisfy the conditions previouslydefined. Local averaging by mean filtering may be carried out on thevariances of those frames that fall inside the window 730, in order toeliminate slight local variations in the variance characteristics thatmay yield false detection. In another embodiment, the window constraintmay be removed, and fade detection may be carried out until the statedconditions are no longer satisfied. In the preferred embodiment, thevalues for ΔHV, ΔHV_(max), and W are preset to Δ  HV = 0.1 × Var(i)Δ  HV_(max) = 32 × Var(i) W = 5

where Var(i) is the computed color histogram variance of frame I. Thesevalues have been shown to yield excellent performance on video sequencescontaining a wide variety of content The final result of this fadedetection process 150 is a list 155 of fade segment locations within thevideo sequence 110, each identified by a start frame and end framenumber.

Dissolve Segment Detection

Referring again to FIG. 1, the video sequence 110 is analyzed to detect165 dissolve temporal segments. Any of the known methods for detectingdissolve temporal segments can be employed. For example, Alattar (U.S.Pat. No. 5,283,645) discloses a method for the compression of dissolvesegments in digital video sequences. In that method, the dissolvesegments are detected prior to compression by analyzing the temporalfunction of interframe pixel variance. Plotting this function reveals aconcave upward parabola in the presence of a dissolve temporal segment.Detection of a dissolve temporal segment is therefore accomplished bydetecting its associated parabola which is present the temporal functionof interframe pixel variance. Those skilled in the art will recognizethat other known methods of characterizing a dissolve temporal segmentmay be employed without departing from the scope of the invention. Thefinal result of this dissolve detection process 160 is a list 165 offade segment locations within the video sequence 110, each identified bya start frame and end frame number.

Refine and Combine Locations

After detection of the four types of temporal segments, the resultingfour lists of temporal segment locations are refined and combined 180 toproduce a single list 130 of the locations of the individual temporalsegments contained in the video sequence 110. In the refinement process180, each detected shot boundary location is checked against thedetected fade segment locations, uniform segment locations, and dissolvesegment locations. If any frame that has been detected as a shotboundary has also been flagged as part of a fade, dissolve, or uniformsegment, that frame is removed from the list of shot boundary locations.Additionally, adjacent shot boundaries that are closer than a predefinednumber of frames, i.e., the minimum number of frames required to call atemporal segment a shot, are dropped. Spurious shot boundaries that aredetected as a result of sudden increases in frame luminancecharacteristics are eliminated by a flash detection process. Flashdetection involves discarding the shot boundary locations where a suddenincrease in frame luminance is registered for the duration of a singleframe. Such frames exist, for example, in outdoor scene where lightningis present. In the flash detection process, the frame statistics of theframe immediately prior to and following such a frame are observed todetermine whether the frame color content remains constant. If this isthe case, the sudden luminance change is labeled as a flash and does notsignal the beginning of a new temporal segment. In the preferredembodiment, the mean of the frame luminance is used as the framestatistic for flash detection. After the refinement process is complete,the four lists of temporal segment locations are combined to produce alist 130 of temporal segment locations (see FIG. 8).

In the preferred embodiment described hereinabove, the frame colorhistogram difference and frame pixel difference metrics are computed forthe entire video sequence 110 prior to clustering in order to producethe list of potential shot boundary locations. This is an acceptableapproach for video sequences that can be processed off-line. For videosequences which required more immediate results or for video sequencesof long duration, an alternative embodiment of the invention computesthese frame difference metrics from frames within smaller temporalregions (windows) to provide a “semi-on-the-fly” implementation. Thelength of the temporal window can a predetermined amount, measured inframes or seconds. The only requirement is that within the temporalwindow there exist at least one true camera shot boundary for theclustering process to work properly. Alternatively, the temporal windowlength can be computed so as to insure that there exists at least onetrue shot boundary within the window. In this embodiment, the varianceof the color histogram difference is computed at every frame as it isprocessed. The running mean and variance of this metric is computedsequentially as the frames of the video sequence are processed. At eachsignificant shot boundary in the video sequence, the running variancevalue will show a local maximum value due to the significant change inthe color histogram difference metric at this temporal location. Whenthe number of local maxima is greater than LM, the temporal windowlength for the first window is set to encompass all frames up to thatpoint and the data for the two difference metrics (color histogramdifference and frame pixel difference) are passed into the clusteringprocess as described hereinbefore. The running mean and variance valueare reset and the process continues from that point to determine thelength of the next temporal window. This process continues until theentire video sequence is processed. In this manner, the video sequenceis parsed into smaller sequences so that the clustering and refinementresults (shot boundary locations) are available for each smallersequence prior to the completion of the processing for the full videosequence. The value of LM can be determined empirically. In thepreferred embodiment, the value of LM is preset to 5. This value insuresthat the class of shot boundaries will be sufficiently populated for thehereinabove described clustering process and has been shown to yieldexcellent performance on video sequences containing a wide variety ofcontent.

In summary, the hereinabove method and system performs accurate andautomatic content-based temporal segmentation of video sequences withoutthe use of content specific thresholds.

The invention has been described with reference to a preferredembodiment. However, it will be appreciated that variations andmodifications can be effected by a person of ordinary skill in the artwithout departing from the scope of the invention.

What is claimed is:
 1. A method for performing content-based temporalsegmentation of video sequences comprising the steps of: (a)transmitting the video sequences to a processor; (b) identifying withinthe video sequences camera shot temporal segments using frame-to-framedifference metrics followed by a clustering operation on theframe-to-frame difference metrics; and (c) outputting a list oflocations within the video sequences of the camera shot temporalsegments based on the clustering operation on the frame-to-framedifference metrics.
 2. The method of claim 1, wherein the frame-to-framedifference metrics includes individually or in combination aframe-to-frame color histogram difference or frame-to-frame pixelintensity difference.
 3. The method of claim 1, wherein the clusteringoperation includes performing a k-means clustering operation.
 4. Themethod as in claim 1, wherein the clustering operation is followed by acamera-shot temporal-segment refinement operation.
 5. The method as inclaim 4, wherein the refinement operation includes a local maximaanalysis of a frame-to-frame color histogram difference based on thecamera shot temporal segment locations from the clustering operation onthe frame-to-frame difference metrics.
 6. The method as in claim 1,wherein step (b) includes segmenting the video sequences into temporallysmaller sequences based upon a pre-analysis of the frame-to-framedifference metrics prior to clustering.
 7. The method as in claim 6,wherein the frame-to-frame difference metrics is the difference of thevariance of the frame color histogram difference.
 8. A computer programproduct, comprising: a computer readable storage medium having acomputer program stored thereon for performing the steps of: (a)transmitting video sequences to a processor; (b) identifying within thevideo sequences camera shot temporal segments using frame-to-framedifference metrics followed by a clustering operation on theframe-to-frame difference metrics; and (c) outputting a list oflocations within the video sequences of the camera shot temporalsegments based on the clustering operation on the frame-to-framedifference metrics.
 9. The computer program product of claim 8, whereinthe frame-to-frame difference metrics includes individually or incombination a frame-to-frame color histogram difference orframe-to-frame pixel intensity difference.
 10. The computer programproduct of claim 8, wherein the clustering operation includes performinga k-means clustering operation.
 11. The computer program product ofclaim 8, wherein the clustering operation is followed by a camera-shottemporal-segment refinement operation.
 12. The computer program productof claim 11, wherein the refinement operation includes a local maximaanalysis of a frame-to-frame color histogram difference based on thecamera shot temporal segments from the clustering operation on theframe-to-fame difference metrics.
 13. The computer program product ofclaim 8, wherein step (b) includes segment the video sequences intotemporally smaller sequences based upon a pre-analysis of theframe-to-fame difference metrics prior to clustering.
 14. The computerprogram product of claim 13, wherein the frame-to-frame differencemetrics is the variance of the frame color histogram difference.